Abstract

The Genetic and Statistical Analysis Core (GSAC) has three primary sets of responsibilities, which may be viewed as specific aims: (a) genetic analyses, (b) data management, and (c) data analysis. The GSAC will act as a clearinghouse for all clinical, genetic and phenotypic data that will be transferred to the DMCC. In this fashion, the GSAC will effectively be the centralized group that tracks all data and their sources. The GSAC is available to the Investigators of the other Projects for: (a) managing and checking the flow of information into the database resources;(b) tracking specimens;(c) genetic analyses;and (d) providing datasets for statistical analysis and support for their analyses. This core structure will allow for optimal coordination of the clinical and basic science research programs components by providing a single centralized resource. All projects will use this Core. Data analysis includes biostatistical data analysis, data cleaning, quality assurance, monitoring patient recruitment and retention, and assistance with preparation of interim reports. Optimal biostatistical analysis also includes involvement in experimental design issues. The GSAC will also assist in preparation of manuscripts and presentations.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54NS065705-02
Application #
8137696
Study Section
Special Emphasis Panel (ZRG1)
Project Start
Project End
Budget Start
2010-07-01
Budget End
2011-06-30
Support Year
2
Fiscal Year
2010
Total Cost
$85,903
Indirect Cost

  Institution

Name
University of California San Francisco
Department
Type
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143

  Publications

Wellman, Rebecca J; Cho, Su Bin; Singh, Pratibha et al. (2018) G?q and hyper-phosphorylated ERK expression in Sturge-Weber syndrome leptomeningeal blood vessel endothelial cells. Vasc Med :1358863X18786068
Morrison, Melanie A; Payabvash, Seyedmehdi; Chen, Yicheng et al. (2018) A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning. Neuroimage Clin 20:498-505
Walcott, Brian P; Winkler, Ethan A; Zhou, Sirui et al. (2018) Identification of a rare BMP pathway mutation in a non-syndromic human brain arteriovenous malformation via exome sequencing. Hum Genome Var 5:18001
Pawlikowska, Ludmila; Nelson, Jeffrey; Guo, Diana E et al. (2018) Association of common candidate variants with vascular malformations and intracranial hemorrhage in hereditary hemorrhagic telangiectasia. Mol Genet Genomic Med 6:350-356
De la Torre, Alejandro J; Luat, Aimee F; Juhász, Csaba et al. (2018) A Multidisciplinary Consensus for Clinical Care and Research Needs for Sturge-Weber Syndrome. Pediatr Neurol 84:11-20
Meybodi, Ali Tayebi; Kim, Helen; Nelson, Jeffrey et al. (2018) Surgical Treatment vs Nonsurgical Treatment for Brain Arteriovenous Malformations in Patients with Hereditary Hemorrhagic Telangiectasia: A Retrospective Multicenter Consortium Study. Neurosurgery 82:35-47
Kasthuri, Raj S; Montifar, Megan; Nelson, Jeffrey et al. (2017) Prevalence and predictors of anemia in hereditary hemorrhagic telangiectasia. Am J Hematol :
Zou, Xiaowei; Hart, Blaine L; Mabray, Marc et al. (2017) Automated algorithm for counting microbleeds in patients with familial cerebral cavernous malformations. Neuroradiology 59:685-690
Tang, Alan T; Choi, Jaesung P; Kotzin, Jonathan J et al. (2017) Endothelial TLR4 and the microbiome drive cerebral cavernous malformations. Nature 545:305-310
Strickland, Corinne D; Eberhardt, Steven C; Bartlett, Mary R et al. (2017) Familial Cerebral Cavernous Malformations Are Associated with Adrenal Calcifications on CT Scans: An Imaging Biomarker for a Hereditary Cerebrovascular Condition. Radiology 284:443-450

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